LibBi is used for state-space modelling and Bayesian inference on high-performance computer hardware, including multi-core CPUs, many-core GPUs (graphics processing units) and distributed-memory clusters.
The staple methods of LibBi are based on sequential Monte Carlo (SMC), also known as particle filtering. These methods include particle Markov chain Monte Carlo (PMCMC) and SMC^2. Other methods include the extended Kalman filter and some parameter optimisation routines.
LibBi consists of a C++ template library, as well as a parser and compiler, written in Perl, for its own modelling language.
- Changes to previous version:
Initial Announcement on mloss.org.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- URL: Project Homepage
- Supported Operating Systems: Linux, Mac Os X
- Data Formats: Netcdf
- Tags: Particle Filter, Gpu, Bayesian, Gpgpu, High Performance Computing, Particle Markov Chain Monte Carlo, Sequential Monte Carlo, State Space Model
- Archive: download here
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